Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry

Levent Çallı · S Mohamed Kasim

10 citations0/0 files storedAcademic Platform Journal of Engineering and Smart Systems

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Customer Service Quality and Loyalty AI and HR Technologies

Abstract

Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP (enterprise resource planning) company with a software as a service (SaaS) business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 10 citations exist upstream.

References

2000Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models 2008Handling class imbalance in customer churn prediction 2015A comparison of machine learning techniques for customer churn prediction 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting 2008Customer churn prediction using improved balanced random forests 2010Building comprehensible customer churn prediction models with advanced rule induction techniques

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.